Reading comprehension (RC) is a widely studied topic in Natural Language Processing (NLP) due to its value in human-machine interaction.
In past relevant research, a variety of large-scale RC datasets were proposed, e.g., CNN/DailyMail [1]}, SQuAD [2]}, NewsQA [3]}, CoQA [4]} and DROP [5]}. With a large number of annotations, these datasets make training end-to-end deep neural models possible [6]}, [7]}. Recent studies also show that BERT [8]} model achieves higher answer accuracy than human on SQuAD.
